Why Wasn’t I Promoted? Exploring the Ambiguity of Linguistic Signals in Academic Promotion Documents
Bibliographic record
Abstract
Academics are continuously making decisions on how to shape their career with particular focus paid to research output, teaching quality, and service involvement to achieve recognition in the form of advancement in rank. For many academics, these career decisions are influenced by the institution's expectations, normally communicated via the university promotion policies. However, these decisions become increasingly difficult to make when faculty are unclear about the expectation the university is signalling. Taking a signalling theory perspective, this study explores the clarity and ambiguity surrounding promotion criteria from a sample of institutions from Ireland, the US, the UK, Canada, New Zealand, and Australia for senior teaching- and research-active faculty positions. Content analysis was applied to 376 individual rank promotion documents to determine what these institutions required individual faculty to show for advancement in rank. It was found that promotion documents contain high levels of ambiguity, specifically regarding the measures used to assess each of the promotion requirements. It was also found that while the literature guides academics on how to build their careers quantitatively, institutions favor a qualitative approach to assess academic productivity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.131 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".